{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"mmfall.ipynb","provenance":[],"collapsed_sections":[],"machine_shape":"hm"},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"jVd1ZFyxjgRJ","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1595795270218,"user_tz":420,"elapsed":257,"user":{"displayName":"Feng Jin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiEcRo6r7oXlZhOdQ0Q-iAWlAEVzGkRtbD3TKfgog=s64","userId":"04447613980258406201"}},"outputId":"4f477442-f4cf-43f0-edb5-e7fa3ecb3fd9"},"source":["from google.colab import drive\n","drive.mount('/content/drive')"],"execution_count":20,"outputs":[{"output_type":"stream","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"dQhXzRQkPVO8","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1595795274767,"user_tz":420,"elapsed":3332,"user":{"displayName":"Feng Jin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiEcRo6r7oXlZhOdQ0Q-iAWlAEVzGkRtbD3TKfgog=s64","userId":"04447613980258406201"}}},"source":["# Author: Feng Jin. Contact info: fengjin@email.arizona.edu\n","import argparse, os\n","import matplotlib.pyplot as plt\n","import numpy as np\n","import random as rn\n","import tensorflow as tf\n","from keras import backend as K\n","from keras import optimizers\n","from keras.layers import Input, Dense, Flatten, Lambda, Concatenate, Reshape, \\\n","    TimeDistributed, LSTM, RepeatVector, SimpleRNN, Activation\n","from keras.models import Model, load_model\n","from keras.callbacks import TensorBoard\n","from keras.losses import mse\n","from keras.utils import plot_model\n","\n","import matplotlib.pylab as pylab\n","params = {'legend.fontsize': 'x-large',\n","        #   'figure.figsize': (19.2, 10.8),\n","         'axes.labelsize': 'x-large',\n","         'axes.titlesize':'x-large',\n","         'xtick.labelsize':'x-large',\n","         'ytick.labelsize':'x-large'}\n","pylab.rcParams.update(params)\n","\n","class data_preproc:\n","    def __init__(self):\n","        # One motion pattern is as (self.frames_per_pattern, self.points_per_frame, self.features_per_point)\n","        self.frames_per_pattern     = 10 # For 1 second for 10 fps radar rate\n","        self.points_per_frame       = 64 # We want to oversample every radar frame to 64 points while keeping the mean and variance the same\n","        self.features_per_point     = 4  # The radar can provides us (x, y, z, Doppler, RCS), but we only keep the first four feature, i.e. (x, y, z, Doppler)\n","\n","        # Train and test data split ratio\n","        self.split_ratio            = 0.8\n","\n","        # Rotation matrix due to tilt angle\n","        tilt_angle  = 10.0 # degrees\n","        self.height = 1.8   # meters\n","        self.rotation_matrix = np.array([[1.0, 0.0, 0.0],\\\n","                                        [0.0, np.cos(np.deg2rad(tilt_angle)), np.sin(np.deg2rad(tilt_angle))],\\\n","                                        [0.0, -np.sin(np.deg2rad(tilt_angle)), np.cos(np.deg2rad(tilt_angle))]])\n","             \n","    def load_bin(self, binfile_path, fortrain=True):\n","        # Record centroid history for analysis purpose\n","        centroidX_his = []\n","        centroidY_his = []\n","        centroidZ_his = []\n","\n","        # Load the recorded bin file\n","        raw_pointcloud = np.load(binfile_path, allow_pickle=True)\n","\n","        # Accumulated the motion patterns with (self.frames_per_pattern) frames\n","        total_pattern = []\n","        for idx in range(len(raw_pointcloud)-self.frames_per_pattern):\n","            total_pattern.append(raw_pointcloud[idx : (idx + self.frames_per_pattern)])\n","\n","        # Original point vector in the .bin file:\n","        #   [frame number, point ID, target ID, \\\\ Basic information\n","        #   centroidX, centroidY, centroidZ, centroidVelocityX, centroidVelocityY, centroidVelocityZ, \\\\ Centorid information\n","        #   range, azimuth angle, elevation angle, Doppler, SNR, noise level] \\\\ Point information\n","        # Extract the feature vector (delta_x, delta_y, z, D, pointRCS) from the original point vector and do data oversampling proposed in the paper\n","        total_processed_pattern = []\n","        total_processed_no_norm = []\n","        for pattern in total_pattern:\n","            # Get the centroid information from the very first frame in a pattern and do coordiante transformation\n","            # As the centroid is already in the radar Cartesian coordinates, we just need to transfer it to the ground Cartesian coordinates\n","            centroidX, centroidY, centroidZ, centroidVx, centroidVy, centroidVz = pattern[0][0][3], pattern[0][0][4], pattern[0][0][5], pattern[0][0][6], pattern[0][0][7], pattern[0][0][8]\n","            results      = np.matmul(self.rotation_matrix, np.array([centroidX,centroidY,centroidZ]))\n","            centroidX    = results[0]\n","            centroidY    = results[1]\n","            centroidZ    = results[2] + self.height\n","\n","            # Record the centroid history over time\n","            centroidX_his.append(centroidX)\n","            centroidY_his.append(centroidY)\n","            centroidZ_his.append(centroidZ)\n","\n","            processed_pattern  = []\n","            for frame in pattern:\n","                processed_frame = []\n","                for point in frame:        \n","                    # Get the original point information.\n","                    pointR, pointAZ, pointEL, pointD, pointSNR, pointNoise = point[9], point[10], point[11], point[12], point[13], point[14]\n","\n","                    # Get the point's position in the Cartesian coord and then do coordiante transformation\n","                    pointX      = pointR*np.cos(pointEL)*np.sin(pointAZ)\n","                    pointY      = pointR*np.cos(pointEL)*np.cos(pointAZ)\n","                    pointZ      = pointR*np.sin(pointEL)\n","                    results     = np.matmul(self.rotation_matrix, np.array([pointX, pointY, pointZ]))\n","                    pointX      = results[0]\n","                    pointY      = results[1]\n","                    pointZ      = results[2] + self.height\n","                    \n","                    # Subtract the point's position from the centroid in the very first frame in a motion pattern\n","                    delta_x     = pointX - centroidX\n","                    delta_y     = pointY - centroidY\n","                    delta_z     = pointZ\n","                    delta_D     = pointD\n","                    pointRCS    = 4*10*np.log10(pointR) + pointSNR*0.1 + pointNoise*0.1 # in dBsm\n","\n","                    # Form the feature vector for each frame\n","                    feature_vector = [delta_x, delta_y, delta_z, delta_D, pointRCS]\n","                    processed_frame.append(feature_vector[0:self.features_per_point]) # Only keep 3D spatial info and the Doppler\n","                processed_pattern.append(processed_frame)\n","                # Do the data oversampling proposed in the paper\n","                processed_pattern_oversampled = self.proposed_oversampling(processed_pattern)\n","            total_processed_pattern.append(processed_pattern_oversampled)\n","\n","        total_processed_pattern_np = np.array(total_processed_pattern)\n","        \n","        # Train and test split\n","        split_idx   = int(total_processed_pattern_np.shape[0]*self.split_ratio)\n","        traindata   = total_processed_pattern_np[0:split_idx]\n","        testdata    = total_processed_pattern_np[split_idx:]\n","\n","        if fortrain is True: # For training, need data split to obtain both training and testing dataset\n","            print(\"INFO: Total normal motion pattern data shape: \" + str(total_processed_pattern_np.shape))\n","            print(\"INFO: Training motion pattern data shape\" + str(traindata.shape))\n","            print(\"INFO: Testing motion pattern data shape\" + str(testdata.shape))\n","            return traindata, testdata\n","        else: # For inference on anomaly dataset\n","            print(\"INFO: Total inference motion pattern data shape: \" + str(total_processed_pattern_np.shape))\n","            return total_processed_pattern, centroidZ_his\n","\n","    def proposed_oversampling(self, processed_pointcloud):\n","        # # Check the input\n","        # point_list = []\n","        # for frame in processed_pointcloud:\n","        #     point_list.extend(frame)\n","        # point_list_np  = np.array(point_list)\n","        # assert (point_list_np.shape[-1] == self.features_per_point), (\"ERROR: Input processed_pointcloud has different feature length rather than %s!\" %(self.features_per_point))\n","\n","        # Do the data oversampling\n","        processed_pointcloud_oversampled = []\n","        for frame in processed_pointcloud:\n","            frame_np = np.array(frame)\n","            # Check if it's empty frame\n","            N = self.points_per_frame\n","            M = frame_np.shape[0]\n","            assert (M != 0), \"ERROR: empty frame detected!\"\n","            # Rescale and padding\n","            mean        = np.mean(frame_np, axis=0)\n","            sigma       = np.std(frame_np, axis=0)\n","            frame_np    = np.sqrt(N/M)*frame_np + mean - np.sqrt(N/M)*mean # Rescale\n","            frame_oversampled = frame_np.tolist()\n","            frame_oversampled.extend([mean]*(N-M)) # Padding with mean\n","            # # Check if mean and sigma keeps the same. Comment for saving time.\n","            # new_mean    = np.mean(np.array(frame_oversampled), axis=0)\n","            # new_sigma   = np.std(np.array(frame_oversampled), axis=0)\n","            # assert np.sum(np.abs(new_mean-mean))<1e-5, (\"ERROR: Mean rescale and padding error!\")\n","            # assert np.sum(np.abs(new_sigma-sigma))<1e-5, (\"ERROR: Sigma rescale and padding error!\")\n","            processed_pointcloud_oversampled.append(frame_oversampled)\n","\n","        processed_pointcloud_oversampled_np = np.array(processed_pointcloud_oversampled)\n","        assert (processed_pointcloud_oversampled_np.shape[-2] == self.points_per_frame), (\"ERROR: The new_frame_data has different number of points per frame rather than %s!\" %(self.points_per_frame))    \n","        assert (processed_pointcloud_oversampled_np.shape[-1] == self.features_per_point), (\"ERROR: The new_frame_data has different feature length rather than %s!\" %(self.features_per_point))    \n","\n","        return processed_pointcloud_oversampled_np\n","\n","class autoencoder_mdl:\n","    def __init__(self, model_dir):\n","        self.model_dir = model_dir\n","\n","    # Variational Recurrent Autoencoder (HVRAE)\n","    def HVRAE_train(self, train_data, test_data):\n","        # In one motion pattern we have\n","        n_frames       = 10\n","        n_points       = 64\n","        n_features     = 4\n","        \n","        # Dimension is going down for encoding. Decoding is just a reflection of encoding.\n","        n_intermidiate    = 64\n","        n_latentdim       = 16\n","        \n","        # Define input\n","        inputs                  = Input(shape=(n_frames, n_points, n_features))\n","        input_flatten           = TimeDistributed(Flatten(None))(inputs)\n","\n","        # VAE: q(z|X). Input: motion pattern. Output: mean and log(sigma^2) for q(z|X).\n","        input_flatten           = TimeDistributed(Dense(n_intermidiate, activation='tanh'))(input_flatten)\n","        Z_mean                  = TimeDistributed(Dense(n_latentdim, activation=None), name='qzx_mean')(input_flatten)\n","        Z_log_var               = TimeDistributed(Dense(n_latentdim, activation=None), name='qzx_log_var')(input_flatten)\n","        def sampling(args): # Instead of sampling from Q(z|X), sample epsilon = N(0,I), z = z_mean + sqrt(var) * epsilon\n","            Z_mean, Z_log_var   = args\n","            batch_size          = K.shape(Z_mean)[0]\n","            n_frames            = K.int_shape(Z_mean)[1]\n","            n_latentdim         = K.int_shape(Z_mean)[2]\n","            # For reproducibility, we set the seed=37\n","            epsilon             = K.random_normal(shape=(batch_size, n_frames, n_latentdim), mean=0., stddev=1.0, seed=None)\n","            Z                   = Z_mean + K.exp(0.5*Z_log_var) * epsilon # The reparameterization trick\n","            return  Z\n","        # VAE: sampling z ~ q(z|X) using reparameterization trick. Output: samples of z.\n","        Z                       = Lambda(sampling)([Z_mean, Z_log_var])\n","\n","        # RNN Autoencoder. Output: reconstructed z.\n","        encoder_feature         = SimpleRNN(n_latentdim, activation='tanh', return_sequences=False)(Z)\n","        decoder_feature         = RepeatVector(n_frames)(encoder_feature)\n","        decoder_feature         = SimpleRNN(n_latentdim, activation='tanh', return_sequences=True)(decoder_feature)\n","        decoder_feature         = Lambda(lambda x: tf.reverse(x, axis=[-2]))(decoder_feature)\n","\n","        # VAE: p(X|z). Output: mean and log(sigma^2) for p(X|z).\n","        X_latent                = TimeDistributed(Dense(n_intermidiate, activation='tanh'))(decoder_feature)\n","        pXz_mean                = TimeDistributed(Dense(n_features, activation=None))(X_latent)\n","        pXz_logvar              = TimeDistributed(Dense(n_features, activation=None))(X_latent)\n","\n","        # Reshape the output. Output: (n_frames, n_points, n_features*2).\n","        # In each frame, every point has a corresponding mean vector with length of n_features and a log(sigma^2) vector with length of n_features.\n","        pXz                     = Concatenate()([pXz_mean, pXz_logvar])\n","        pXz                     = TimeDistributed(RepeatVector(n_points))(pXz)\n","        outputs                 = TimeDistributed(Reshape((n_points, n_features*2)))(pXz)\n","\n","        # Build the model\n","        self.HVRAE_mdl = Model(inputs, outputs)\n","        print(self.HHVRAE_mdl.summary())\n","\n","        # Calculate HVRAE loss proposed in the paper\n","        def HVRAE_loss(y_true, y_pred):\n","            batch_size      = K.shape(y_true)[0]\n","            n_frames        = K.shape(y_true)[1]\n","            n_features      = K.shape(y_true)[-1]\n","\n","            mean            = y_pred[:, :, :, :n_features]\n","            logvar          = y_pred[:, :, :, n_features:]\n","            var             = K.exp(logvar)\n","\n","            y_true_reshape  = K.reshape(y_true, (batch_size, n_frames, -1)) \n","            mean            = K.reshape(mean, (batch_size, n_frames, -1)) \n","            var             = K.reshape(var, (batch_size, n_frames, -1)) \n","            logvar          = K.reshape(logvar, (batch_size, n_frames, -1)) \n","\n","            # E[log_pXz] ~= log_pXz\n","            log_pXz         = K.square(y_true_reshape - mean)/var\n","            log_pXz         = K.sum(0.5*log_pXz, axis=-1)\n","            \n","            # KL divergence between q(z|x) and p(z)\n","            kl_loss         = -0.5 * K.sum(1 + Z_log_var - K.square(Z_mean) - K.exp(Z_log_var), axis=-1)\n","\n","            # HVRAE loss is log_pXz + kl_loss\n","            HVRAE_loss        = K.mean(log_pXz + kl_loss) # Do mean over batches\n","            return HVRAE_loss\n","\n","        # Define stochastic gradient descent optimizer Adam\n","        adam    = optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, amsgrad=False)\n","        # Compile the model\n","        self.HVRAE_mdl.compile(optimizer=adam, loss=HVRAE_loss)\n","\n","        # Train the model\n","        self.HVRAE_mdl.fit(train_data, train_data, # Train on the normal training dataset in an unsupervised way\n","                epochs=5,\n","                batch_size=8,\n","                shuffle=False,\n","                validation_data=(test_data, test_data), # Testing on the normal tesing dataset\n","                callbacks=[TensorBoard(log_dir=(self.model_dir + \"/../model_history/HVRAE_online\"))])\n","        self.HVRAE_mdl.save(self.model_dir + 'HVRAE_mdl_online.h5')\n","        # plot_model(self.HVRAE_mdl, show_shapes =True, to_file=self.model_dir+'HVRAE_mdl_online.png')\n","        print(\"INFO: Training is done!\")\n","        print(\"*********************************************************************\")\n","\n","    def HVRAE_predict(self, inferencedata):\n","        K.clear_session()\n","\n","        def sampling_predict(args): # Instead of sampling from Q(z|X), sample epsilon = N(0,I), z = z_mean + sqrt(var) * epsilon\n","            Z_mean, Z_log_var   = args\n","            batch_size          = K.shape(Z_mean)[0]\n","            n_frames            = K.int_shape(Z_mean)[1]\n","            n_latentdim         = K.int_shape(Z_mean)[2]\n","            # For reproducibility, we set the seed=37\n","            epsilon             = K.random_normal(shape=(batch_size, n_frames, n_latentdim), mean=0., stddev=1.0, seed=None)\n","            Z                   = Z_mean + K.exp(0.5*Z_log_var) * epsilon # The reparameterization trick\n","            return  Z\n","\n","        # Load saved model\n","        model = load_model(self.model_dir + 'HVRAE_mdl.h5', compile = False, custom_objects={'sampling': sampling_predict, 'tf': tf})\n","        adam  = optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, amsgrad=False)\n","        # Because we do not train the model, the loss function does not matter here.\n","        # Adding MSE as loss is omly for compiling the model. We can add any loss function here.\n","        # This is because our HVRAE loss function is customized function, we can not simply add it here.\n","        # We will define and call the HVRAE loss later.\n","        model.compile(optimizer=adam, loss=mse)\n","        print(\"INFO: Model loaded from \" + self.model_dir + 'HVRAE_mdl.h5')\n","\n","        get_z_mean_model    = Model(inputs=model.input, outputs=model.get_layer('qzx_mean').output)\n","        get_z_log_var_model = Model(inputs=model.input, outputs=model.get_layer('qzx_log_var').output)\n","\n","        # Numpy version of HVRAE_loss function\n","        def HVRAE_loss(y_true, y_pred, Z_mean, Z_log_var):\n","            batch_size      = y_true.shape[0]\n","            n_frames        = y_true.shape[1]\n","            n_features      = y_true.shape[-1]\n","\n","            mean            = y_pred[:, :, :, :n_features]\n","            logvar          = y_pred[:, :, :, n_features:]\n","            var             = np.exp(logvar)\n","\n","            y_true_reshape  = np.reshape(y_true, (batch_size, n_frames, -1)) \n","            mean            = np.reshape(mean, (batch_size, n_frames, -1)) \n","            var             = np.reshape(var, (batch_size, n_frames, -1)) \n","            logvar          = np.reshape(logvar, (batch_size, n_frames, -1)) \n","\n","            # E[log_pXz] ~= log_pXz\n","            # log_pXz       = K.square(y_true_reshape-mean)/var + logvar\n","            log_pXz         = np.square(y_true_reshape - mean)/var\n","            log_pXz         = np.sum(0.5*log_pXz, axis=-1)\n","            \n","            # KL divergence between q(z|x) and p(z)\n","            kl_loss         = -0.5 * np.sum(1 + Z_log_var - np.square(Z_mean) - np.exp(Z_log_var), axis=-1)\n","\n","            # HVRAE loss is log_pXz + kl_loss\n","            HVRAE_loss        = np.mean(log_pXz + kl_loss) # Do mean over batches\n","            return HVRAE_loss\n","\n","        print(\"INFO: Start to predict...\")\n","        prediction_history  = []\n","        loss_history        = []\n","        for pattern in inferencedata:\n","            pattern             = np.expand_dims(pattern, axis=0)\n","            current_prediction  = model.predict(pattern, batch_size=1)\n","            predicted_z_mean    = get_z_mean_model.predict(pattern, batch_size=1)\n","            predicted_z_log_var = get_z_log_var_model.predict(pattern, batch_size=1)\n","            # Call the HVRAE_loss function\n","            # The HVRAE_loss function input is: \n","            # Model input motion pattern, model output mean and logvar of p(X|z), mean of q(z|X), logvar of q(z|X)\n","            current_loss        = HVRAE_loss(pattern, current_prediction, predicted_z_mean, predicted_z_log_var)\n","            loss_history.append(current_loss)\n","        print(\"INFO: Prediction is done!\")\n","\n","        return loss_history\n","\n","    # Baseline #1: HVRAE_SL with simplified loss function\n","    def HVRAE_SL_train(self, train_data, test_data):\n","        # In one motion pattern we have\n","        n_frames       = 10\n","        n_points       = 64\n","        n_features     = 4\n","        \n","        # Dimension is going down for encoding. Decoding is just a reflection of encoding.\n","        n_intermidiate    = 64\n","        n_latentdim       = 16\n","        \n","        # Define input\n","        inputs                  = Input(shape=(n_frames, n_points, n_features))\n","        input_flatten           = TimeDistributed(Flatten(None))(inputs)\n","\n","        # VAE: q(z|X). Input: motion pattern. Output: mean and log(sigma^2) for q(z|X).\n","        input_flatten           = TimeDistributed(Dense(n_intermidiate, activation='tanh'))(input_flatten)\n","        Z_mean                  = TimeDistributed(Dense(n_latentdim, activation=None), name='qzx_mean')(input_flatten)\n","        Z_log_var               = TimeDistributed(Dense(n_latentdim, activation=None), name='qzx_log_var')(input_flatten)\n","        def sampling(args): # Instead of sampling from Q(z|X), sample epsilon = N(0,I), z = z_mean + sqrt(var) * epsilon\n","            Z_mean, Z_log_var   = args\n","            batch_size          = K.shape(Z_mean)[0]\n","            n_frames            = K.int_shape(Z_mean)[1]\n","            n_latentdim         = K.int_shape(Z_mean)[2]\n","            # For reproducibility, we set the seed=37\n","            epsilon             = K.random_normal(shape=(batch_size, n_frames, n_latentdim), mean=0., stddev=1.0, seed=None)\n","            Z                   = Z_mean + K.exp(0.5*Z_log_var) * epsilon # The reparameterization trick\n","            return  Z\n","        # VAE: sampling z ~ q(z|X) using reparameterization trick. Output: samples of z.\n","        Z                       = Lambda(sampling)([Z_mean, Z_log_var])\n","\n","        # RNN Autoencoder. Output: reconstructed z.\n","        encoder_feature         = SimpleRNN(n_latentdim, activation='tanh', return_sequences=False)(Z)\n","        decoder_feature         = RepeatVector(n_frames)(encoder_feature)\n","        decoder_feature         = SimpleRNN(n_latentdim, activation='tanh', return_sequences=True)(decoder_feature)\n","        decoder_feature         = Lambda(lambda x: tf.reverse(x, axis=[-2]))(decoder_feature)\n","\n","        # VAE: p(X|z). Output: mean for p(X|z).\n","        X_latent                = TimeDistributed(Dense(n_intermidiate, activation='tanh'))(decoder_feature)\n","        pXz_mean                = TimeDistributed(Dense(n_features, activation=None))(X_latent)\n","\n","        # Reshape the output. Output: (n_frames, n_points, n_features*2).\n","        # In each frame, every point has a corresponding mean vector with length of n_features.\n","        pXz_mean                = TimeDistributed(RepeatVector(n_points))(pXz_mean)\n","        outputs                 = TimeDistributed(Reshape((n_points, n_features)))(pXz_mean)\n","\n","        # Build the model\n","        self.HVRAE_SL_mdl = Model(inputs, outputs)\n","        print(self.HVRAE_SL_mdl.summary())\n","\n","        # Calculate HVRAE loss proposed in the paper\n","        def HVRAE_loss(y_true, y_pred):\n","            batch_size      = K.shape(y_true)[0]\n","            n_frames        = K.shape(y_true)[1]\n","            n_features      = K.shape(y_true)[-1]\n","\n","            mean            = y_pred\n","\n","            y_true_reshape  = K.reshape(y_true, (batch_size, n_frames, -1)) \n","            mean            = K.reshape(mean, (batch_size, n_frames, -1)) \n","\n","            # E[log_pXz] ~= log_pXz\n","            # In this case, the loss is MSE\n","            log_pXz         = K.square(y_true_reshape - mean)\n","            log_pXz         = K.sum(0.5*log_pXz, axis=-1)\n","            log_pXz         = mse(y_true_reshape, mean)\n","            \n","            # KL divergence between q(z|x) and p(z)\n","            kl_loss         = -0.5 * K.sum(1 + Z_log_var - K.square(Z_mean) - K.exp(Z_log_var), axis=-1)\n","\n","            # HVRAE loss is log_pXz + kl_loss\n","            HVRAE_loss        = K.mean(log_pXz + kl_loss) # Do mean over batches\n","            return HVRAE_loss\n","\n","        # Define stochastic gradient descent optimizer Adam\n","        adam    = optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, amsgrad=False)\n","        # Compile the model\n","        self.HVRAE_SL_mdl.compile(optimizer=adam, loss=HVRAE_loss)\n","\n","        # Train the model\n","        self.HVRAE_SL_mdl.fit(train_data, train_data, # Train on the normal training dataset in an unsupervised way\n","                epochs=5,\n","                batch_size=8,\n","                shuffle=False,\n","                validation_data=(test_data, test_data), # Testing on the normal tesing dataset\n","                callbacks=[TensorBoard(log_dir=(self.model_dir + \"/../model_history/HVRAE_SL_online\"))])\n","        self.HVRAE_SL_mdl.save(self.model_dir + 'HVRAE_SL_mdl_online.h5')\n","        # plot_model(self.HVRAE_SL_mdl, show_shapes =True, to_file=self.model_dir+'HVRAE_SL_mdl_online.png')\n","        print(\"INFO: Training is done!\")\n","        print(\"*********************************************************************\")  \n","\n","    def HVRAE_SL_predict(self, inferencedata):\n","        K.clear_session()\n","\n","        def sampling_predict(args): # Instead of sampling from Q(z|X), sample epsilon = N(0,I), z = z_mean + sqrt(var) * epsilon\n","            Z_mean, Z_log_var   = args\n","            batch_size          = K.shape(Z_mean)[0]\n","            n_frames            = K.int_shape(Z_mean)[1]\n","            n_latentdim         = K.int_shape(Z_mean)[2]\n","            # For reproducibility, we set the seed=37\n","            epsilon             = K.random_normal(shape=(batch_size, n_frames, n_latentdim), mean=0., stddev=1.0, seed=None)\n","            Z                   = Z_mean + K.exp(0.5*Z_log_var) * epsilon # The reparameterization trick\n","            return  Z \n","\n","        # Load the saved model\n","        model = load_model(self.model_dir + 'HVRAE_SL_mdl.h5', compile = False, custom_objects={'sampling': sampling_predict, 'tf': tf})\n","        adam  = optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, amsgrad=False)\n","        # Because we do not train the model, the loss function does not matter here.\n","        # Adding MSE as loss is omly for compiling the model. We can add any loss function here.\n","        # This is because our HVRAE loss function is customized function, we can not simply add it here.\n","        # We will define and call the HVRAE loss later.\n","        model.compile(optimizer=adam, loss=mse)\n","        print(\"INFO: Model loaded from \" + self.model_dir + 'HVRAE_SL_mdl.h5')\n","\n","        get_z_mean_model    = Model(inputs=model.input, outputs=model.get_layer('qzx_mean').output)\n","        get_z_log_var_model = Model(inputs=model.input, outputs=model.get_layer('qzx_log_var').output)\n","\n","        # Numpy version of HVRAE_loss function\n","        def HVRAE_loss(y_true, y_pred, Z_mean, Z_log_var):\n","            batch_size      = y_true.shape[0]\n","            n_frames        = y_true.shape[1]\n","            n_features      = y_true.shape[-1]\n","\n","            mean            = y_pred\n","\n","            y_true_reshape  = np.reshape(y_true, (batch_size, n_frames, -1)) \n","            mean            = np.reshape(mean, (batch_size, n_frames, -1)) \n","\n","            # E[log_pXz] ~= log_pXz\n","            # In this case, the loss is MSE\n","            # log_pXz         = np.square(y_true_reshape - mean)\n","            # log_pXz         = np.sum(0.5*log_pXz, axis=-1)\n","            log_pXz         = (np.square(y_true_reshape - mean)).mean(axis=-1)\n","            \n","            # KL divergence between q(z|x) and p(z)\n","            kl_loss         = -0.5 * np.sum(1 + Z_log_var - np.square(Z_mean) - np.exp(Z_log_var), axis=-1)\n","\n","            # HVRAE loss is log_pXz + kl_loss\n","            HVRAE_loss        = np.mean(log_pXz + kl_loss) # Do mean over batches\n","            return HVRAE_loss\n","\n","        print(\"INFO: Start to predict...\")\n","        prediction_history  = []\n","        loss_history        = []\n","        for pattern in inferencedata:\n","            pattern             = np.expand_dims(pattern, axis=0)\n","            current_prediction  = model.predict(pattern, batch_size=1)\n","            predicted_z_mean    = get_z_mean_model.predict(pattern, batch_size=1)\n","            predicted_z_log_var = get_z_log_var_model.predict(pattern, batch_size=1)\n","            # Call the HVRAE_loss function\n","            # The HVRAE_loss function input is: \n","            # Model input motion pattern, model output mean and logvar of p(X|z), mean of q(z|X), logvar of q(z|X)\n","            current_loss        = HVRAE_loss(pattern, current_prediction, predicted_z_mean, predicted_z_log_var)\n","            loss_history.append(current_loss)\n","        print(\"INFO: Prediction is done!\")\n","\n","        return loss_history\n","\n","    # Baseline #2: Recurrent AE\n","    def RAE_train(self, train_data, test_data):\n","        # In one motion pattern we have\n","        n_frames       = 10\n","        n_points       = 64\n","        n_features     = 4\n","        \n","        # Dimension is going down for encoding. Decoding is just a reflection of encoding.\n","        n_intermidiate    = 64\n","        n_latentdim       = 16\n","        \n","        # Define input\n","        inputs                  = Input(shape=(n_frames, n_points, n_features))\n","        input_flatten           = TimeDistributed(Flatten(None))(inputs)\n","\n","        # Embedding or feature compression\n","        encoder_feature         = TimeDistributed(Dense(n_intermidiate, activation='tanh'))(input_flatten)\n","        encoder_feature         = TimeDistributed(Dense(n_latentdim, activation='tanh'))(encoder_feature)\n","\n","        # RNN Autoencoder.\n","        encoder_feature         = SimpleRNN(n_latentdim, activation='tanh', return_sequences=False)(encoder_feature)\n","        decoder_feature         = RepeatVector(n_frames)(encoder_feature)\n","        decoder_feature         = SimpleRNN(n_latentdim, activation='tanh', return_sequences=True)(decoder_feature)\n","        decoder_feature         = Lambda(lambda x: tf.reverse(x, axis=[-2]))(decoder_feature)\n","\n","        # Embedding or feature compression\n","        decoder_feature         = TimeDistributed(Dense(n_intermidiate, activation='tanh'))(decoder_feature)\n","        decoder_feature         = TimeDistributed(Dense(n_points*n_features, activation='tanh'))(decoder_feature)\n","\n","        # Reshape\n","        outputs                 = TimeDistributed(Reshape((n_points, n_features)))(decoder_feature)\n","\n","        # Build the model\n","        self.RAE_mdl = Model(inputs, outputs)\n","        print(self.RAE_mdl.summary())\n","\n","        # Define stochastic gradient descent optimizer Adam\n","        adam    = optimizers.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, amsgrad=False)\n","        # Compile the model\n","        self.RAE_mdl.compile(optimizer=adam, loss=mse)\n","\n","        # Train the model\n","        self.RAE_mdl.fit(train_data, train_data, # Train on the normal training dataset in an unsupervised way\n","                epochs=5,\n","                batch_size=8,\n","                shuffle=False,\n","                validation_data=(test_data, test_data), # Testing on the normal tesing dataset\n","                callbacks=[TensorBoard(log_dir=(self.model_dir + \"/../model_history/RAE_online\"))])\n","        self.RAE_mdl.save(self.model_dir + 'RAE_mdl_online.h5')\n","        # plot_model(self.RAE_mdl, show_shapes =True, to_file=self.model_dir+'RAE_mdl_online.png')\n","        print(\"INFO: Training is done!\")\n","        print(\"*********************************************************************\")  \n","\n","    def RAE_predict(self, inferencedata):\n","        K.clear_session()\n","\n","        # Load the saved model\n","        model = load_model(self.model_dir + 'RAE_mdl.h5', compile = True, custom_objects={'tf': tf})\n","        # plot_model(model, show_shapes =True, to_file=self.model_dir+'RAE_model.png')\n","        print(\"INFO: Start to predict...\")\n","        prediction_history  = []\n","        loss_history        = []\n","        for pattern in inferencedata:\n","            pattern             = np.expand_dims(pattern, axis=0)\n","            current_loss        = model.test_on_batch(pattern, pattern)\n","            loss_history.append(current_loss)\n","        print(\"INFO: Prediction is done!\")\n","\n","        return loss_history\n","\n","class compute_metric:\n","    def __init__(self):\n","        pass\n","\n","    def detect_falls(self, loss_history, centroidZ_history, threshold):\n","        assert len(loss_history) == len(centroidZ_history), \"ERROR: The length of loss history is different than the length of centroidZ history!\"\n","        seq_len                 = len(loss_history)\n","        win_len                 = 20 # Detection window length on account of 2 seconds for 10 fps radar rate\n","        centroidZ_dropthres     = 0.6\n","        i                       = int(win_len/2)\n","        detected_falls_idx      = []\n","        # Firstly, detect the fall centers based on the centroidZ drop\n","        while i < (seq_len - win_len/2): \n","            detection_window_middle  = i\n","            detection_window_lf_edge = int(detection_window_middle - win_len/2)\n","            detection_window_rh_edge = int(detection_window_middle + win_len/2)\n","            # Search the centroidZ drop\n","            if centroidZ_history[detection_window_lf_edge] - centroidZ_history[detection_window_rh_edge] >= centroidZ_dropthres:\n","                detected_falls_idx.append(int(detection_window_middle))\n","            i += 1\n","\n","        # Secondly, if a sequence of fall happen within a window less than win_len, we combine these falls into one fall centered at the middle of this sequence\n","        i = 0\n","        processed_detected_falls_idx = []\n","        while i < len(detected_falls_idx):\n","            j = i\n","            while True:\n","                if j == len(detected_falls_idx):\n","                    break \n","                if detected_falls_idx[j] - detected_falls_idx[i] > win_len:\n","                    break\n","                j += 1\n","            processed_detected_falls_idx.append(int((detected_falls_idx[i] + detected_falls_idx[j-1])/2))\n","            i = j\n","\n","        # Thirdly, find id there is an anomaly level (or loss history) spike in the detection window\n","        ones_idx                    = np.argwhere(np.array(loss_history)>=threshold).flatten()\n","        fall_binseq                 = np.zeros(seq_len)\n","        fall_binseq[ones_idx]       = 1\n","        final_detected_falls_idx    = []\n","        i = 0 \n","        while i < len(processed_detected_falls_idx):\n","            detection_window_middle  = int(processed_detected_falls_idx[i])\n","            detection_window_lf_edge = int(detection_window_middle - win_len/2)\n","            detection_window_rh_edge = int(detection_window_middle + win_len/2)\n","            if 1 in fall_binseq[detection_window_lf_edge:detection_window_rh_edge]:\n","                final_detected_falls_idx.append(processed_detected_falls_idx[i])\n","            i += 1\n","        \n","        return final_detected_falls_idx, len(processed_detected_falls_idx)\n","\n","    def find_tpfpfn(self, detected_falls_idx, gt_falls_idx):\n","        n_detected_falls    = len(detected_falls_idx)\n","        falls_tp            = []\n","        falls_fp            = []\n","        falls_fn            = list(gt_falls_idx)\n","        win_len             = 20\n","        for i in range(n_detected_falls):\n","            n_gt_falls      = len(falls_fn)\n","            j               = 0\n","            while j < n_gt_falls:\n","                # Find a gt fall index whose window covers the detected fall index, so it's true positive\n","                if int(falls_fn[j]-win_len/2) <= detected_falls_idx[i] <= int(falls_fn[j]+win_len/2):\n","                    # Remove the true positive from the gt_falls_idx list, finally only false negative remains\n","                    falls_fn.pop(j)  \n","                    falls_tp.append(i)\n","                    break\n","                j += 1\n","            # Dn not find a gt fall index whose window covers the detected fall index, so it's false positive\n","            if j == n_gt_falls:\n","                falls_fp.append(i)\n","\n","        return falls_tp, falls_fp, falls_fn\n","\n","    def cal_roc(self, loss_history, centroidZ_history, gt_falls_idx):\n","        n_gt_falls = len(gt_falls_idx)\n","        print(\"How many falls?\", n_gt_falls)\n","        tpr, fpr = [], []\n","        for threshold in np.arange(0.0, 10.0, 0.1):\n","            detected_falls_idx, _           = self.detect_falls(loss_history, centroidZ_history, threshold)\n","            falls_tp, falls_fp, falls_fn    = self.find_tpfpfn(detected_falls_idx, gt_falls_idx)\n","            # Save the true positve rate for this threshold.\n","            tpr.append(len(falls_tp)/n_gt_falls)\n","            # Save the number of false positve, or missed fall detection, for this threshold\n","            fpr.append(len(falls_fp))\n","        return tpr, fpr"],"execution_count":21,"outputs":[]},{"cell_type":"code","metadata":{"id":"WxcaQPOSTPn7","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1595795276173,"user_tz":420,"elapsed":330,"user":{"displayName":"Feng Jin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiEcRo6r7oXlZhOdQ0Q-iAWlAEVzGkRtbD3TKfgog=s64","userId":"04447613980258406201"}}},"source":["if __name__ == '__main__':\n","    project_path = '/content/drive/My Drive/Colab/mmfall/'\n","    \n","    # # Generate the motion pattern from the original normal dataset. The proposed oversampling method is included.\n","    # # Uncomment if you want to regenerate the data. It may take a long time.\n","    # #####################################################################################################\n","    # print(\"*********************************************************************\")\n","    # # Load the normal data file and preprocess the data\n","    # print(\"INFO: Start preprocessing the normal training dataset...\")\n","    # train_data, test_data= data_preproc().load_bin(project_path + 'data/DS0/DS0.npy', fortrain=True)\n","    # # Save the normal training and testing dataset\n","    # np.save(project_path + 'data/normal_train_data', np.array(train_data))\n","    # print(\"INFO: The train_data is saved in \" + save_datapath + 'data/normal_train_data' + \".npy\")\n","    # np.save(project_path + 'data/normal_test_data', np.array(test_data))\n","    # print(\"INFO: The test_data is saved in \" + save_datapath + 'data/normal_test_data' + \".npy\")\n","    # #####################################################################################################"],"execution_count":22,"outputs":[]},{"cell_type":"code","metadata":{"id":"WqtRHGRn-M-d","colab_type":"code","colab":{},"executionInfo":{"status":"ok","timestamp":1595795277935,"user_tz":420,"elapsed":306,"user":{"displayName":"Feng Jin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiEcRo6r7oXlZhOdQ0Q-iAWlAEVzGkRtbD3TKfgog=s64","userId":"04447613980258406201"}}},"source":["    # # Uncomment if you want to retrain the models. It may take a long time. And you may expect slightly changes in the results as training progress has lot of random things.\n","    # # Or you can skip this section, go to next and load the saved models to check the results.\n","    # # Train and save the model\n","    # print(\"INFO: Load train/test data...\")\n","    # train_data      = np.load(project_path + 'data/normal_train_data.npy', allow_pickle=True)\n","    # test_data       = np.load(project_path + 'data/normal_test_data.npy', allow_pickle=True)\n","    # print(\"INFO: Start HVRAE/HVRAE_SL/RAE model training and testing...\")\n","    # model = autoencoder_mdl(model_dir = (project_path + 'saved_model/'))\n","    # model.HVRAE_train(train_data, test_data)\n","    # model.HVRAE_SL_train(train_data, test_data)\n","    # model.RAE_train(train_data, test_data)"],"execution_count":23,"outputs":[]},{"cell_type":"code","metadata":{"id":"k-7UEKHWiNpA","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1595795299771,"user_tz":420,"elapsed":20761,"user":{"displayName":"Feng Jin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiEcRo6r7oXlZhOdQ0Q-iAWlAEVzGkRtbD3TKfgog=s64","userId":"04447613980258406201"}},"outputId":"d48b7cef-90d8-4f24-e9ec-c307607d0c4b"},"source":["    # Load inference dataset and the ground truth timesheet\n","    inferencedata_file                      = project_path + 'data/DS1/DS1_4falls'\n","    inferencedata, centroidZ_history        = data_preproc().load_bin(inferencedata_file + '.npy', fortrain=False)\n","    # Ground truth time index file exists\n","    if os.path.exists(inferencedata_file + '.csv'): \n","        gt_falls_idx                        = np.genfromtxt(inferencedata_file + '.csv', delimiter=',').astype(int)\n","    # Maybe this file doesn't contain any falls\n","    else: \n","        gt_falls_idx                        = []\n","\n","    # Load the models\n","    model                                   = autoencoder_mdl(model_dir = project_path + 'saved_model/')\n","    \n","    HVRAE_loss_history                       = model.HVRAE_predict(inferencedata)\n","    plt.figure()\n","    plt.ylim(0.0, 2.2)\n","    plt.xlabel(\"Time in Seconds\")\n","    time_step = np.arange(0, len(centroidZ_history))*0.1 # 0.1 seconds per frame\n","    plt.plot(time_step, centroidZ_history, linewidth=2, label='Centroid Height')\n","    plt.plot(time_step, HVRAE_loss_history, linewidth=2, label='Anomaly Level')\n","    plt.legend(loc=\"upper right\")\n","    plt.title('HVRAE Results')\n","    plt.savefig(inferencedata_file+'_HVRAE_prediction.png')\n","    plt.show()\n","\n","    HVRAE_SL_loss_history                    = model.HVRAE_SL_predict(inferencedata)\n","    plt.figure()\n","    plt.ylim(0.0, 2.2)\n","    plt.xlabel(\"Time in Seconds\")\n","    time_step = np.arange(0, len(centroidZ_history))*0.1 # 0.1 seconds per frame\n","    plt.plot(time_step, centroidZ_history, linewidth=2, label='Centroid Height')\n","    plt.plot(time_step, HVRAE_SL_loss_history, linewidth=2, label='Anomaly Level')\n","    plt.legend(loc=\"upper right\")\n","    plt.title('HVRAE_SL Results')\n","    plt.savefig(inferencedata_file+'_HVRAE_SL_prediction.png')\n","    plt.show()\n","\n","    RAE_loss_history                        = model.RAE_predict(inferencedata)\n","    plt.figure()\n","    plt.ylim(0.0, 2.2)\n","    plt.xlabel(\"Time in Seconds\")\n","    time_step = np.arange(0, len(centroidZ_history))*0.1 # 0.1 seconds per frame\n","    plt.plot(time_step, centroidZ_history, linewidth=2, label='Centroid Height')\n","    plt.plot(time_step, RAE_loss_history, linewidth=2, label='Anomaly Level')\n","    plt.legend(loc=\"upper right\")\n","    plt.title('RAE Results')\n","    plt.savefig(inferencedata_file+'_RAE_prediction.png')\n","    plt.show()"],"execution_count":24,"outputs":[{"output_type":"stream","text":["INFO: Total inference motion pattern data shape: (1078, 10, 64, 4)\n","INFO: Model loaded from /content/drive/My Drive/Colab/mmfall/saved_model/HVRAE_mdl.h5\n","INFO: Start to predict...\n","INFO: Prediction is done!\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["INFO: Model loaded from /content/drive/My Drive/Colab/mmfall/saved_model/HVRAE_SL_mdl.h5\n","INFO: Start to predict...\n","INFO: Prediction is done!\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["INFO: Start to predict...\n","INFO: Prediction is done!\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"BA1ESWcaltM8","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1595795386279,"user_tz":420,"elapsed":19305,"user":{"displayName":"Feng Jin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiEcRo6r7oXlZhOdQ0Q-iAWlAEVzGkRtbD3TKfgog=s64","userId":"04447613980258406201"}},"outputId":"d75e2398-cac4-4e48-d378-1b515fd8db07"},"source":["    # Load inference dataset and the ground truth timesheet\n","    inferencedata_file                      = project_path + 'data/DS1/DS1_4normal'\n","    inferencedata, centroidZ_history        = data_preproc().load_bin(inferencedata_file + '.npy', fortrain=False)\n","    # Ground truth time index file exists\n","    if os.path.exists(inferencedata_file + '.csv'): \n","        gt_falls_idx                        = np.genfromtxt(inferencedata_file + '.csv', delimiter=',').astype(int)\n","    # Maybe this file doesn't contain any falls\n","    else: \n","        gt_falls_idx                        = []\n","\n","    # Load the models\n","    model                                   = autoencoder_mdl(model_dir = project_path + 'saved_model/')\n","    \n","    HVRAE_loss_history                       = model.HVRAE_predict(inferencedata)\n","    plt.figure()\n","    plt.ylim(0.0, 2.2)\n","    plt.xlabel(\"Time in Seconds\")\n","    time_step = np.arange(0, len(centroidZ_history))*0.1 # 0.1 seconds per frame\n","    plt.plot(time_step, centroidZ_history, linewidth=2, label='Centroid Height')\n","    plt.plot(time_step, HVRAE_loss_history, linewidth=2, label='Anomaly Level')\n","    plt.legend(loc=\"upper right\")\n","    plt.title('HVRAE Results')\n","    plt.savefig(inferencedata_file+'_HVRAE_prediction.png')\n","    plt.show()\n","\n","    HVRAE_SL_loss_history                    = model.HVRAE_SL_predict(inferencedata)\n","    plt.figure()\n","    plt.ylim(0.0, 2.2)\n","    plt.xlabel(\"Time in Seconds\")\n","    time_step = np.arange(0, len(centroidZ_history))*0.1 # 0.1 seconds per frame\n","    plt.plot(time_step, centroidZ_history, linewidth=2, label='Centroid Height')\n","    plt.plot(time_step, HVRAE_SL_loss_history, linewidth=2, label='Anomaly Level')\n","    plt.legend(loc=\"upper right\")\n","    plt.title('HVRAE_SL Results')\n","    plt.savefig(inferencedata_file+'_HVRAE_SL_prediction.png')\n","    plt.show()\n","\n","    RAE_loss_history                        = model.RAE_predict(inferencedata)\n","    plt.figure()\n","    plt.ylim(0.0, 2.2)\n","    plt.xlabel(\"Time in Seconds\")\n","    time_step = np.arange(0, len(centroidZ_history))*0.1 # 0.1 seconds per frame\n","    plt.plot(time_step, centroidZ_history, linewidth=2, label='Centroid Height')\n","    plt.plot(time_step, RAE_loss_history, linewidth=2, label='Anomaly Level')\n","    plt.legend(loc=\"upper right\")\n","    plt.title('RAE Results')\n","    plt.savefig(inferencedata_file+'_RAE_prediction.png')\n","    plt.show()"],"execution_count":26,"outputs":[{"output_type":"stream","text":["INFO: Total inference motion pattern data shape: (1033, 10, 64, 4)\n","INFO: Model loaded from /content/drive/My Drive/Colab/mmfall/saved_model/HVRAE_mdl.h5\n","INFO: Start to predict...\n","INFO: Prediction is done!\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["INFO: Model loaded from /content/drive/My Drive/Colab/mmfall/saved_model/HVRAE_SL_mdl.h5\n","INFO: Start to predict...\n","INFO: Prediction is done!\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}},{"output_type":"stream","text":["INFO: Start to predict...\n","INFO: Prediction is done!\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"6N3uNK8miahj","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":491},"executionInfo":{"status":"ok","timestamp":1595796362311,"user_tz":420,"elapsed":970444,"user":{"displayName":"Feng Jin","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GiEcRo6r7oXlZhOdQ0Q-iAWlAEVzGkRtbD3TKfgog=s64","userId":"04447613980258406201"}},"outputId":"fd28e658-bd6f-4afc-c20d-70311beb7a34"},"source":["    # Load inference dataset and the ground truth timesheet\n","    inferencedata_file                      = project_path + 'data/DS2/DS2'\n","    inferencedata, centroidZ_history        = data_preproc().load_bin(inferencedata_file + '.npy', fortrain=False)\n","    # Ground truth time index file exists\n","    if os.path.exists(inferencedata_file + '.csv'): \n","        gt_falls_idx                        = np.genfromtxt(inferencedata_file + '.csv', delimiter=',').astype(int)\n","    # Maybe this file doesn't contain any falls\n","    else: \n","        gt_falls_idx                        = []\n","\n","    # Load the models\n","    model                                   = autoencoder_mdl(model_dir = project_path + 'saved_model/')\n","    HVRAE_loss_history                       = model.HVRAE_predict(inferencedata)\n","    HVRAE_SL_loss_history                    = model.HVRAE_SL_predict(inferencedata)\n","    RAE_loss_history                        = model.RAE_predict(inferencedata)\n","    \n","    # For performace evaluation\n","    calculator                              = compute_metric()\n","    HVRAE_tpr, HVRAE_fp_total                 = calculator.cal_roc(HVRAE_loss_history, centroidZ_history, gt_falls_idx)\n","    HVRAE_SL_tpr, HVRAE_SL_fp_total           = calculator.cal_roc(HVRAE_SL_loss_history, centroidZ_history, gt_falls_idx)\n","    RAE_tpr, RAE_fp_total                   = calculator.cal_roc(RAE_loss_history, centroidZ_history, gt_falls_idx)\n","\n","    # Plot Receiver operating characteristic (ROC) curves\n","    plt.figure()\n","    plt.xlim(-0.1, 10)\n","    plt.xticks(np.arange(0, 11, 1))\n","    plt.ylim(0.0, 1.1)\n","    plt.scatter(HVRAE_fp_total, HVRAE_tpr, c='r')\n","    plt.plot(HVRAE_fp_total, HVRAE_tpr, c='r', linewidth=2, label='HVRAE ROC')\n","    plt.xlim(-0.1, 10)\n","    plt.xticks(np.arange(0, 11, 1))\n","    plt.ylim(0.0, 1.1)\n","    plt.scatter(HVRAE_SL_fp_total, HVRAE_SL_tpr, c='b')\n","    plt.plot(HVRAE_SL_fp_total, HVRAE_SL_tpr, c='b', linewidth=2, label='HVRAE_SL ROC')\n","    plt.xlim(-0.1, 10)\n","    plt.xticks(np.arange(0, 11, 1))\n","    plt.ylim(0.0, 1.1)\n","    plt.scatter(RAE_fp_total, RAE_tpr, c='g')\n","    plt.plot(RAE_fp_total, RAE_tpr, c='g', linewidth=2, label='RAE ROC')\n","    plt.legend(loc=\"lower right\")\n","    plt.xlabel(\"Number od False Alarms\")\n","    plt.ylabel(\"True Fall Detection Rate\")\n","    plt.savefig(inferencedata_file + '_ROC.png')\n","    plt.show()"],"execution_count":27,"outputs":[{"output_type":"stream","text":["INFO: Total inference motion pattern data shape: (60278, 10, 64, 4)\n","INFO: Model loaded from /content/drive/My Drive/Colab/mmfall/saved_model/HVRAE_mdl.h5\n","INFO: Start to predict...\n","INFO: Prediction is done!\n","INFO: Model loaded from /content/drive/My Drive/Colab/mmfall/saved_model/HVRAE_SL_mdl.h5\n","INFO: Start to predict...\n","INFO: Prediction is done!\n","INFO: Start to predict...\n","INFO: Prediction is done!\n","How many falls? 50\n","How many falls? 50\n","How many falls? 50\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"6CZLjTusuESr","colab_type":"code","colab":{}},"source":[""],"execution_count":null,"outputs":[]}]}